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Greatest papers with code

Inductive Relation Prediction by Subgraph Reasoning

ICML 2020 kkteru/grail

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.

INDUCTIVE KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPH EMBEDDING QUESTION ANSWERING RELATIONAL REASONING

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

NeurIPS 2019 alisadeghian/DRUM

Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities.

INDUCTIVE KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION

RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools

16 Sep 2019IBM/RuDaS

Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form.

INDUCTIVE KNOWLEDGE GRAPH COMPLETION INDUCTIVE LOGIC PROGRAMMING KNOWLEDGE GRAPHS RELATIONAL REASONING

Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs

29 Jun 2020irokin/RuleHierarchy

Many systems have been developed in recent years to mine logical rules from large-scale Knowledge Graphs (KGs), on the grounds that representing regularities as rules enables both the interpretable inference of new facts, and the explanation of known facts.

INDUCTIVE KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPHS